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Development of Intelligent Stock Trading System Using Pattern Independent Predictor and Turning Point Matrix

Author

Listed:
  • Yoojeong Song

    (Sookmyung Women’s University)

  • Jae Won Lee

    (Sungshin Women’s University)

  • Jongwoo Lee

    (Sookmyung Women’s University)

Abstract

In this paper, we propose an intelligent stock trading system utilizing moving average patterns which reflect short-term fluctuations in stock price and turning point matrix which represents the long term stock price fluctuation. The core technique of our intelligent stock trading system are as follows. The first technique is the composition of pattern-intensive data based on the fluctuation pattern of the moving average line. We constructed pattern-specific predictors for each of the four patterns using the golden cross, the rising transition point and arrangement of the moving average lines. The four specific patterns we defined were developed based on the analysis method commonly used in stock chart analysis. This pattern specific predictors makes it possible to construct pattern-intensive data, and we proved that the pattern-intensive data can improve the performance of the stock price prediction model through experiments. Our second core technique is the Fibonacci sequence-based turning point matrix. Using this, the process of long-term stock price fluctuations can be expressed in a compact way. We used a combination of pattern-specific predictors and turning point matrix to construct input features that could reflect the pattern of constant fluctuations in stock prices in the short and long term. Finally, we have developed a stock trading system that uses this input features to predict future price changes and obtain profit above market returns

Suggested Citation

  • Yoojeong Song & Jae Won Lee & Jongwoo Lee, 2022. "Development of Intelligent Stock Trading System Using Pattern Independent Predictor and Turning Point Matrix," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 27-38, January.
    • Handle: RePEc:kap:compec:v:59:y:2022:i:1:d:10.1007_s10614-020-10066-6
    DOI: 10.1007/s10614-020-10066-6
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    References listed on IDEAS

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    1. Xingyu Zhou & Zhisong Pan & Guyu Hu & Siqi Tang & Cheng Zhao, 2018. "Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-11, April.
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    4. Chen, Tai-Liang & Cheng, Ching-Hsue & Jong Teoh, Hia, 2007. "Fuzzy time-series based on Fibonacci sequence for stock price forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 380(C), pages 377-390.
    5. Dixon, Matthew & Klabjan, Diego & Bang, Jin Hoon, 2017. "Classification-based financial markets prediction using deep neural networks," Algorithmic Finance, IOS Press, vol. 6(3-4), pages 67-77.
    6. Ayodele Ariyo Adebiyi & Aderemi Oluyinka Adewumi & Charles Korede Ayo, 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-7, March.
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    Cited by:

    1. Wenbin Hu & Junzi Zhou, 2024. "Trading Signal Survival Analysis: A Framework for Enhancing Technical Analysis Strategies in Stock Markets," Computational Economics, Springer;Society for Computational Economics, vol. 64(6), pages 3473-3507, December.

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